Find Academic Supervisors

• Big Data Analytics
• Machine Learning
• Data Mining
• +7 more

Dr. Sikha S Bagui is a Distinguished University Professor in the Department of Computer Science at the Hal Marcus College of Science and Engineering, University of West Florida . She served as the former Chair of Computer Science and was the Founding Director of the Center for Cybersecurity . Her research spans database design, Big Data analytics, machine learning, and cybersecurity . Research Focus: Machine Learning, Data Mining, Network Traffic Analysis, Graph Databases, and Resampling Techniques for Imbalanced Data Awards: Askew Fellow (2018–2021), multiple Excellence in Teaching and Distinguished Research Awards (2001–2024) Contributions: Authored books on databases/SQL (translated internationally), developed the UWF-ZeekData datasets for cybersecurity research, and served as Associate Editor for multiple journals Tools & Frameworks: Active in Hadoop, Spark, Memgraph, and MITRE ATT&CK-based threat modeling Publication Trends: Recent work focuses on MITRE ATT&CK datasets , graph-based cybersecurity , resampling rare attacks , and educational impacts in computing . Her research bridges theoretical and applied domains, including clinical decision support systems and K-12 computer science education. Scientific Awards: Askew Fellow (Reubin O’D. Askew Institute for Multidisciplinary Studies, 2018–2021) Excellence in Teaching and Advising Award (UWF, 2012) Distinguished Research and Creative Activities Award (UWF, 2007, 2012) Excellence in Undergraduate Teaching and Advising Award (UWF, 2001–2006) Leadership & Service: Directed the Center for Cybersecurity, contributed to journals as Associate Editor, and engaged in initiatives like NCWIT Aspirations in Computing and the Association for Women in Computing.

Rasmus T. VarneskovView profile

Professor

• Econometrics
• Financial Economics
• Time Series Analysis
• +3 more

Rasmus T. Varneskov is a Professor in the Department of Finance at Copenhagen Business School, Denmark, with an active research profile evidenced by publications through 2025. His work bridges theoretical econometrics and financial applications, focusing on methodological innovations for market volatility and return prediction. Research interests include: Econometrics Financial Economics Time Series Analysis Volatility Modeling Asset Pricing Structural Change His recent publications (2022-2025) reveal a concentrated focus on bootstrap techniques for volatility estimation and robust inference in predictive regressions. Key contributions address challenges in high-frequency financial data, Laplace transforms for volatility metrics, and handling persistent autoregressive processes with structural breaks, primarily through advanced statistical modeling. Scientific Awards: No awards, fellowships, or medals are documented in the provided text. Advising: Varneskov has supervised 5 works (likely graduate theses), though student names and grant funding specifics are absent. His mentorship activity aligns with his role as a research-active faculty member in econometrics.

Thomas NaglerView profile

Professor

• Mathematical Statistics
• Machine Learning
• High-dimensional Dependence Models
• +3 more

Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.

Yili HongView profile

Professor

• Machine Learning
• Reliability Analysis
• Survival Analysis
• +4 more

Yili Hong is a Professor in the Department of Statistics at Virginia Tech's College of Science. Her research focuses on statistical methodologies in reliability analysis, machine learning, survival analysis, and spatial data analysis. She holds a Ph.D. from Iowa State University (2009), with prior degrees from the same institution and the University of Science and Technology of China. Hong has been recognized with awards such as the ASA Physical and Engineering Sciences Award (2017) and the DuPont Young Professor Award (2011). She serves on editorial boards for journals like Technometrics and Journal of Quality Technology, emphasizing contributions to statistical reliability and big data applications. Her work bridges theoretical statistics with practical challenges in engineering and public health, including studies on Lyme disease emergence in Virginia through spatial modeling. Education: Ph.D. in Statistics, Iowa State University, 2009 M.S. in Statistics, Iowa State University, 2005 B.S. in Statistics, University of Science and Technology of China, 2004 Research interests span machine learning robustness, reliability of AI systems, degradation analysis, and statistical computing. Notable achievements include pioneering work on mixture experiments for AI algorithm evaluation and statistical methods for high-performance computing variability. Her recent projects address the reliability of autonomous systems and AI safety through recurrent event data analysis. Awards and recognitions highlight her contributions to statistical methodology and industrial applications. Editorial activities reflect her leadership in advancing statistical practice through peer-reviewed journals.

Daan CrommelinView profile

Professor

• Uncertainty Quantification
• Stochastic Modeling
• Data-Driven Modeling
• +6 more

Daan Crommelin holds a part-time professorship in Numerical Analysis and Dynamical Systems at the KdV Institute for Mathematics, University of Amsterdam, and is a senior researcher at CWI Amsterdam's Scientific Computing group. He serves on CWI's management team and previously led its Scientific Computing group (2013–2021). His research focuses on stochastic modeling of multiscale systems, uncertainty quantification, and rare event analysis, with applications in climate science, renewable energy, and fluid dynamics. Crommelin combines methods from scientific computing, applied probability, and dynamical systems to address challenges in atmosphere-ocean-climate modeling. He has contributed to projects like the EU-funded VECMA initiative for exascale computing and collaborated on superparameterization techniques for climate models. His work also extends to epidemic modeling and computational chemistry. Crommelin earned his PhD in 2003 from Utrecht University, with a thesis co-supervised by KNMI, and holds an MSc in theoretical physics and an MA in philosophy from the University of Amsterdam.

Sylvain ArlotView profile

Professor

• Statistical Learning
• Non-Parametric Statistics
• Data-Driven Tuning of Learning Algorithms
• +5 more

Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.

• Causal Inference
• Precision Medicine
• Mendelian Randomization
• +5 more

Yunan Wu is an Assistant Professor at the Yau Mathematical Sciences Center, Tsinghua University (since September 2024). Previously, he served as an Assistant Professor at the University of Texas at Dallas (2020–2024) and completed a postdoctoral fellowship in Biostatistics at Yale University’s School of Public Health (2019–2020). He earned his PhD in Statistics from the University of Minnesota (2015–2019), supervised by Prof. Lan Wang, and holds a bachelor’s degree in Mathematics and Physics from Tsinghua University (Beijing, China). His research focuses on **causal inference in precision medicine**, **Mendelian randomization**, **non-parametric/semi-parametric methods**, and **high-dimensional statistical analysis**. He also explores robust machine learning techniques and incorrupted data methodologies. His work bridges theoretical advancements with practical applications in healthcare and biomedical research. Key contributions include the development of tuning-free robust regression approaches (e.g., the TFRE method) and inference frameworks for optimal treatment regimes. His GitHub repositories reflect these efforts, including implementations of robust regression and treatment regime estimation algorithms. He is affiliated with the Yau Mathematical Sciences Center, where he contributes to interdisciplinary research at the intersection of statistics, machine learning, and biomedical sciences. His email is wuyunan@mail.tsinghua.edu.cn , and his office is located in Shuangqing Complex Building A504.

Jérémie LEFEBVREView profile

Assistant Professor

• Market Microstructure
• Financial Regulation
• Stock Market Liquidity
• +3 more

Jérémie Lefebvre is an Assistant Professor of Finance at IÉSEG School of Management in Paris, France. He holds a Ph.D. in Finance from Tilburg University (2011), an M.Phil. in Econometrics (2010), and an MSc in Econometrics from the University of Montreal (2005). His research focuses on market microstructure, financial regulation, and stock market liquidity , with a particular emphasis on legal insider trading dynamics and regulatory policy impacts. He has published extensively in peer-reviewed journals such as International Review of Law and Economics and De Economist . His teaching spans Corporate Finance , Finance Strategy , and Financial Econometrics , across undergraduate and graduate programs including the MSc in Investment Banking and Capital Markets. Prior to IÉSEG, he was a Researcher at the Louvain School of Management (2009–2010). His work bridges legal frameworks and financial markets, analyzing how regulatory changes affect liquidity and market behavior in both developed and emerging economies like Vietnam and the Netherlands. Key research themes include pre-trade disclosure mechanisms , insider trading detection , and cross-market regulatory comparisons . His recent studies explore liquidity impacts of insider transactions in Vietnam’s stock market and the Netherlands’ regulatory environment. He has also developed methodologies for analyzing market reactions using event studies and bootstrap techniques. While no scientific awards are explicitly listed, his publications demonstrate significant contributions to understanding market microstructure and regulatory compliance. His academic advising and grant activities remain unspecified in available records. Lefebvre’s interdisciplinary approach integrates legal, economic, and quantitative perspectives to address contemporary challenges in financial markets.

Joseph RomanoView profile

Professor

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +7 more

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.

Frédéric BERTRANDView profile

Professor

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +5 more

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

Anton WestveldView profile

Senior Lecturer

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +3 more

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

• Computer Vision
• Medical Imaging
• Artificial Intelligence in Healthcare
• +3 more

Alessandro Bevilacqua is an Assistant Professor at the Department of Computer Science and Engineering, University of Bologna. His research focuses on computer vision, medical imaging, and artificial intelligence in healthcare. He leads the Computer Vision Group, which develops advanced algorithms for tumor characterization, radiomics, and automated analysis of pathological images. His work integrates machine learning, quantitative imaging, and clinical applications to improve diagnostic accuracy in oncology. Bevilacqua’s research spans areas such as diffusion-weighted MRI analysis, multiplex immunohistochemistry quantification, and radiomic feature extraction. He collaborates with clinical teams to translate computational methods into practical diagnostic tools. His recent projects include developing AI models for prostate cancer detection, colorectal lesion classification, and predicting treatment responses using imaging biomarkers. Key technical contributions include novel metrics for tumor-peritumour interface analysis, automated workflows for whole-slide imaging, and reproducibility frameworks for radiomic studies. His methodologies address challenges in heterogeneity assessment, clinical significance prediction, and imaging protocol optimization. Academic activities include teaching computer science and engineering courses, mentoring students in AI-driven healthcare projects, and advising on collaborative research initiatives. He maintains an active presence in interdisciplinary research, bridging computational methods with clinical oncology, radiology, and molecular biology.

• Machine Learning
• Kernel Methods
• Model Selection
• +11 more

Gavin Cawley is a Professor in the School of Computing Sciences at the University of East Anglia (UEA), with additional affiliations to the Data Science and AI group, the Centre for Ocean and Atmospheric Sciences, and the Statistics group. His research spans machine learning, bioinformatics, climate modeling, and environmental data analysis. His primary research interests include Machine Learning , Kernel Methods , Model Selection , Bayesian Regularization , Bioinformatics , and Climate Modeling . He has made significant contributions to understanding overfitting in model selection, sparse logistic regression for gene and cancer classification, and time series classification using ensemble methods. His work bridges theoretical machine learning with practical applications in biology, archaeology, and environmental science. The recent trend in his publications shows a strong interdisciplinary focus, combining machine learning with climate science (e.g., Arctic sea ice prediction) and molecular biology (e.g., protein domain movements). His work often involves developing and evaluating statistical models for complex real-world problems, emphasizing robustness, interpretability, and predictive accuracy. While no specific awards are listed in the provided text, his extensive publication record in top journals such as Journal of Machine Learning Research , Bioinformatics , and Neural Networks , along with high citation counts (e.g., over 1,800 citations for his 2010 paper on overfitting), indicates significant recognition in the academic community. He has also contributed to organizing major machine learning challenges, such as the ChaLearn AutoML and Active Learning challenges. He has supervised or collaborated with numerous researchers across disciplines, though specific student names are not listed. His work involves methodological development in model selection, kernel learning, and survival analysis, often applied to biological and environmental datasets. He has been involved in projects related to predictive uncertainty, ozone forecasting, and microbial growth modeling. While no specific lab or team name is mentioned, his affiliations with the Data Science and AI group and the Centre for Ocean and Atmospheric Sciences suggest active participation in interdisciplinary research teams focused on data-driven environmental and biological modeling.

• Cancer Cachexia
• Metabolism and Cancer
• Adipose Tissue Physiology
• +3 more

Serkan Kir is an Assistant Professor in the Department of Molecular Biology and Genetics at Koc University. His research focuses on cancer cachexia, metabolism in oncology, and the physiological interactions between adipose tissue and skeletal muscle. He obtained his PhD from UT Southwestern Medical Center (2011) and a Bachelor's degree from Bilkent University (2006). Education: PhD, UT Southwestern Medical Center, 2011 Bachelor's Degree, Bilkent University, 2006 Research Interests: Cancer cachexia, metabolism, and tissue physiology are central themes in his work. He investigates: Mechanisms of tumor-induced muscle wasting Adipose tissue thermogenesis and browning Cytokine signaling (e.g., interleukin-6 family, Oncostatin M) Role of EDA2R-NIK and PTHrP pathways in cachexia Epigenetic and transcriptional regulation in metabolic disorders Therapeutic targeting of HDACs and receptor signaling Recent Publication Trends: His work spans translational cancer biology, metabolic reprogramming during cachexia, and receptor-ligand interactions driving tissue wasting. Key areas include fibroblast growth factor signaling, bile acid metabolism, and interventions for Duchenne muscular dystrophy.